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 Duration 7 hours

Course Outline

Introduction to Lightweight LLMs

  • Exploring compact model architectures
  • The progression of resource-efficient AI
  • The significance of lightweight models for enterprise environments

Gaining Insight into Nano Banana

  • Core features and underlying design principles
  • Assessing model strengths and constraints
  • Distinguishing Nano Banana from conventional LLMs

Deployment Strategies and Application Scenarios

  • On-device execution and its associated advantages
  • Comparing local and cloud-based inference
  • Determining the optimal deployment approach

Practical Applications in Various Industries

  • Internal automation and knowledge support
  • Customer-facing implementation examples
  • Operational and compliance-focused use cases

Foundations of Integration

  • Reviewing system requirements
  • Considerations for workflow and process alignment
  • Introduction to APIs and the toolchain

Cost Optimization and Operational Efficiency

  • Lowering inference costs through compact models
  • Striking a balance between performance and resource usage
  • Planning for scalable implementation

Governance, Data Privacy, and Risk Oversight

  • Guaranteeing secure on-device operations
  • Comprehending data boundaries and protective measures
  • Ensuring alignment with corporate policies and standards

Readying for Organizational Integration

  • Developing internal expertise and preparedness
  • Evaluating business impact through pilot initiatives
  • Establishing the foundation for wider adoption

Conclusion and Path Forward

Requirements

  • A solid grasp of general IT concepts
  • Proficiency with basic software tools
  • Knowledge of data-driven business workflows

Target Audience

  • IT teams integrating AI capabilities into their operations
  • Business professionals seeking practical AI applications
  • Technology managers assessing on-device LLM strategies

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